Overview

Dataset statistics

Number of variables13
Number of observations517
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)0.8%
Total size in memory52.6 KiB
Average record size in memory104.2 B

Variable types

Numeric11
Categorical2

Warnings

Dataset has 4 (0.8%) duplicate rows Duplicates
rain has 509 (98.5%) zeros Zeros
area has 247 (47.8%) zeros Zeros

Reproduction

Analysis started2021-03-12 01:46:45.955635
Analysis finished2021-03-12 01:46:57.983343
Duration12.03 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

X
Real number (ℝ≥0)

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.669245648
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:58.035355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.313777829
Coefficient of variation (CV)0.4955356825
Kurtosis-1.172330846
Mean4.669245648
Median Absolute Deviation (MAD)2
Skewness0.03624582161
Sum2414
Variance5.353567841
MonotocityNot monotonic
2021-03-12T02:46:58.109373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
491
17.6%
686
16.6%
273
14.1%
861
11.8%
760
11.6%
355
10.6%
148
9.3%
530
 
5.8%
913
 
2.5%
ValueCountFrequency (%)
148
9.3%
273
14.1%
355
10.6%
491
17.6%
530
 
5.8%
ValueCountFrequency (%)
913
 
2.5%
861
11.8%
760
11.6%
686
16.6%
530
 
5.8%

Y
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.299806576
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:58.192392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median4
Q35
95-th percentile6
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.229900403
Coefficient of variation (CV)0.2860362161
Kurtosis1.420553416
Mean4.299806576
Median Absolute Deviation (MAD)1
Skewness0.4172962459
Sum2223
Variance1.512655001
MonotocityNot monotonic
2021-03-12T02:46:58.269408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4203
39.3%
5125
24.2%
674
 
14.3%
364
 
12.4%
244
 
8.5%
96
 
1.2%
81
 
0.2%
ValueCountFrequency (%)
244
 
8.5%
364
 
12.4%
4203
39.3%
5125
24.2%
674
 
14.3%
ValueCountFrequency (%)
96
 
1.2%
81
 
0.2%
674
 
14.3%
5125
24.2%
4203
39.3%

month
Categorical

Distinct12
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
aug
184 
sep
172 
mar
54 
jul
32 
feb
20 
Other values (7)
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowmar
2nd rowoct
3rd rowoct
4th rowmar
5th rowmar
ValueCountFrequency (%)
aug184
35.6%
sep172
33.3%
mar54
 
10.4%
jul32
 
6.2%
feb20
 
3.9%
jun17
 
3.3%
oct15
 
2.9%
apr9
 
1.7%
dec9
 
1.7%
jan2
 
0.4%
Other values (2)3
 
0.6%
2021-03-12T02:46:58.467454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug184
35.6%
sep172
33.3%
mar54
 
10.4%
jul32
 
6.2%
feb20
 
3.9%
jun17
 
3.3%
oct15
 
2.9%
apr9
 
1.7%
dec9
 
1.7%
jan2
 
0.4%
Other values (2)3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a251
16.2%
u233
15.0%
e201
13.0%
g184
11.9%
p181
11.7%
s172
11.1%
r63
 
4.1%
m56
 
3.6%
j51
 
3.3%
l32
 
2.1%
Other values (9)127
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1551
100.0%

Most frequent character per category

ValueCountFrequency (%)
a251
16.2%
u233
15.0%
e201
13.0%
g184
11.9%
p181
11.7%
s172
11.1%
r63
 
4.1%
m56
 
3.6%
j51
 
3.3%
l32
 
2.1%
Other values (9)127
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin1551
100.0%

Most frequent character per script

ValueCountFrequency (%)
a251
16.2%
u233
15.0%
e201
13.0%
g184
11.9%
p181
11.7%
s172
11.1%
r63
 
4.1%
m56
 
3.6%
j51
 
3.3%
l32
 
2.1%
Other values (9)127
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1551
100.0%

Most frequent character per block

ValueCountFrequency (%)
a251
16.2%
u233
15.0%
e201
13.0%
g184
11.9%
p181
11.7%
s172
11.1%
r63
 
4.1%
m56
 
3.6%
j51
 
3.3%
l32
 
2.1%
Other values (9)127
8.2%

day
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
sun
95 
fri
85 
sat
84 
mon
74 
tue
64 
Other values (2)
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowtue
3rd rowsat
4th rowfri
5th rowsun
ValueCountFrequency (%)
sun95
18.4%
fri85
16.4%
sat84
16.2%
mon74
14.3%
tue64
12.4%
thu61
11.8%
wed54
10.4%
2021-03-12T02:46:58.640492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-12T02:46:58.700506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
sun95
18.4%
fri85
16.4%
sat84
16.2%
mon74
14.3%
tue64
12.4%
thu61
11.8%
wed54
10.4%

Most occurring characters

ValueCountFrequency (%)
u220
14.2%
t209
13.5%
s179
11.5%
n169
10.9%
e118
7.6%
f85
 
5.5%
r85
 
5.5%
i85
 
5.5%
a84
 
5.4%
m74
 
4.8%
Other values (4)243
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1551
100.0%

Most frequent character per category

ValueCountFrequency (%)
u220
14.2%
t209
13.5%
s179
11.5%
n169
10.9%
e118
7.6%
f85
 
5.5%
r85
 
5.5%
i85
 
5.5%
a84
 
5.4%
m74
 
4.8%
Other values (4)243
15.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1551
100.0%

Most frequent character per script

ValueCountFrequency (%)
u220
14.2%
t209
13.5%
s179
11.5%
n169
10.9%
e118
7.6%
f85
 
5.5%
r85
 
5.5%
i85
 
5.5%
a84
 
5.4%
m74
 
4.8%
Other values (4)243
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1551
100.0%

Most frequent character per block

ValueCountFrequency (%)
u220
14.2%
t209
13.5%
s179
11.5%
n169
10.9%
e118
7.6%
f85
 
5.5%
r85
 
5.5%
i85
 
5.5%
a84
 
5.4%
m74
 
4.8%
Other values (4)243
15.7%

FFMC
Real number (ℝ≥0)

Distinct106
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.64468085
Minimum18.7
Maximum96.2
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:58.825534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18.7
5-th percentile84.1
Q190.2
median91.6
Q392.9
95-th percentile95.1
Maximum96.2
Range77.5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation5.520110849
Coefficient of variation (CV)0.06089834282
Kurtosis67.06604054
Mean90.64468085
Median Absolute Deviation (MAD)1.3
Skewness-6.575605977
Sum46863.3
Variance30.47162378
MonotocityNot monotonic
2021-03-12T02:46:58.936559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.628
 
5.4%
92.128
 
5.4%
9122
 
4.3%
91.719
 
3.7%
93.716
 
3.1%
92.416
 
3.1%
92.515
 
2.9%
94.814
 
2.7%
90.212
 
2.3%
90.112
 
2.3%
Other values (96)335
64.8%
ValueCountFrequency (%)
18.71
0.2%
50.41
0.2%
53.41
0.2%
63.52
0.4%
68.21
0.2%
ValueCountFrequency (%)
96.22
 
0.4%
96.16
1.2%
962
 
0.4%
95.92
 
0.4%
95.81
 
0.2%

DMC
Real number (ℝ≥0)

Distinct215
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.8723404
Minimum1.1
Maximum291.3
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:59.253631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile14.92
Q168.6
median108.3
Q3142.4
95-th percentile231.1
Maximum291.3
Range290.2
Interquartile range (IQR)73.8

Descriptive statistics

Standard deviation64.04648225
Coefficient of variation (CV)0.5776596941
Kurtosis0.2048217813
Mean110.8723404
Median Absolute Deviation (MAD)34.9
Skewness0.5474977945
Sum57321
Variance4101.951889
MonotocityNot monotonic
2021-03-12T02:46:59.355653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9910
 
1.9%
129.59
 
1.7%
142.48
 
1.5%
231.18
 
1.5%
1377
 
1.4%
126.57
 
1.4%
108.47
 
1.4%
108.37
 
1.4%
35.87
 
1.4%
117.96
 
1.2%
Other values (205)441
85.3%
ValueCountFrequency (%)
1.11
0.2%
2.41
0.2%
32
0.4%
3.21
0.2%
3.61
0.2%
ValueCountFrequency (%)
291.31
 
0.2%
2904
0.8%
287.21
 
0.2%
284.91
 
0.2%
276.34
0.8%

DC
Real number (ℝ≥0)

Distinct219
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.9400387
Minimum7.9
Maximum860.6
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:59.468679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile43.58
Q1437.7
median664.2
Q3713.9
95-th percentile795.3
Maximum860.6
Range852.7
Interquartile range (IQR)276.2

Descriptive statistics

Standard deviation248.0661917
Coefficient of variation (CV)0.4527250688
Kurtosis-0.245243519
Mean547.9400387
Median Absolute Deviation (MAD)80.2
Skewness-1.100445125
Sum283285
Variance61536.83547
MonotocityNot monotonic
2021-03-12T02:46:59.567702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
745.310
 
1.9%
692.69
 
1.7%
698.68
 
1.5%
601.48
 
1.5%
692.38
 
1.5%
715.18
 
1.5%
647.17
 
1.4%
686.57
 
1.4%
706.47
 
1.4%
80.87
 
1.4%
Other values (209)438
84.7%
ValueCountFrequency (%)
7.91
0.2%
9.31
0.2%
15.31
0.2%
15.51
0.2%
15.81
0.2%
ValueCountFrequency (%)
860.61
 
0.2%
855.34
0.8%
849.31
 
0.2%
8441
 
0.2%
825.14
0.8%

ISI
Real number (ℝ≥0)

Distinct119
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.021663443
Minimum0
Maximum56.1
Zeros1
Zeros (%)0.2%
Memory size4.2 KiB
2021-03-12T02:46:59.668724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.5
median8.4
Q310.8
95-th percentile17
Maximum56.1
Range56.1
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.559477175
Coefficient of variation (CV)0.5053920714
Kurtosis21.4580365
Mean9.021663443
Median Absolute Deviation (MAD)2.1
Skewness2.536325266
Sum4664.2
Variance20.78883211
MonotocityNot monotonic
2021-03-12T02:46:59.763745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.623
 
4.4%
7.121
 
4.1%
6.320
 
3.9%
717
 
3.3%
8.417
 
3.3%
6.216
 
3.1%
9.215
 
2.9%
7.514
 
2.7%
8.112
 
2.3%
7.812
 
2.3%
Other values (109)350
67.7%
ValueCountFrequency (%)
01
 
0.2%
0.42
0.4%
0.71
 
0.2%
0.83
0.6%
1.11
 
0.2%
ValueCountFrequency (%)
56.11
 
0.2%
22.71
 
0.2%
22.61
 
0.2%
21.31
 
0.2%
20.34
0.8%

temp
Real number (ℝ≥0)

Distinct192
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.88916828
Minimum2.2
Maximum33.3
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:46:59.865769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.2
Q115.5
median19.3
Q322.8
95-th percentile27.9
Maximum33.3
Range31.1
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.80662535
Coefficient of variation (CV)0.3074050304
Kurtosis0.1361655077
Mean18.88916828
Median Absolute Deviation (MAD)3.6
Skewness-0.3311722373
Sum9765.7
Variance33.71689795
MonotocityNot monotonic
2021-03-12T02:46:59.965791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.48
 
1.5%
19.68
 
1.5%
15.47
 
1.4%
20.67
 
1.4%
23.46
 
1.2%
21.66
 
1.2%
17.86
 
1.2%
18.96
 
1.2%
20.76
 
1.2%
19.16
 
1.2%
Other values (182)451
87.2%
ValueCountFrequency (%)
2.21
 
0.2%
4.21
 
0.2%
4.66
1.2%
4.81
 
0.2%
5.15
1.0%
ValueCountFrequency (%)
33.31
0.2%
33.11
0.2%
32.61
0.2%
32.42
0.4%
32.31
0.2%

RH
Real number (ℝ≥0)

Distinct75
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.28820116
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:47:00.077816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile24
Q133
median42
Q353
95-th percentile77
Maximum100
Range85
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.31746924
Coefficient of variation (CV)0.3684382931
Kurtosis0.438182856
Mean44.28820116
Median Absolute Deviation (MAD)10
Skewness0.8629040079
Sum22897
Variance266.2598024
MonotocityNot monotonic
2021-03-12T02:47:00.176839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2733
 
6.4%
3924
 
4.6%
3520
 
3.9%
4317
 
3.3%
4217
 
3.3%
4516
 
3.1%
3416
 
3.1%
3315
 
2.9%
4015
 
2.9%
4614
 
2.7%
Other values (65)330
63.8%
ValueCountFrequency (%)
152
0.4%
171
 
0.2%
181
 
0.2%
194
0.8%
201
 
0.2%
ValueCountFrequency (%)
1001
0.2%
991
0.2%
971
0.2%
961
0.2%
941
0.2%

wind
Real number (ℝ≥0)

Distinct21
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.017601547
Minimum0.4
Maximum9.4
Zeros0
Zeros (%)0.0%
Memory size4.2 KiB
2021-03-12T02:47:00.269860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.3
Q12.7
median4
Q34.9
95-th percentile7.6
Maximum9.4
Range9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.791652601
Coefficient of variation (CV)0.4459507942
Kurtosis0.05432381711
Mean4.017601547
Median Absolute Deviation (MAD)1.3
Skewness0.571001127
Sum2077.1
Variance3.210019042
MonotocityNot monotonic
2021-03-12T02:47:00.347878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.253
10.3%
3.153
10.3%
451
9.9%
4.948
9.3%
2.744
8.5%
4.541
7.9%
5.441
7.9%
3.640
7.7%
1.831
 
6.0%
5.824
 
4.6%
Other values (11)91
17.6%
ValueCountFrequency (%)
0.41
 
0.2%
0.913
 
2.5%
1.314
 
2.7%
1.831
6.0%
2.253
10.3%
ValueCountFrequency (%)
9.44
 
0.8%
8.91
 
0.2%
8.58
1.5%
85
 
1.0%
7.614
2.7%

rain
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02166344294
Minimum0
Maximum6.4
Zeros509
Zeros (%)98.5%
Memory size4.2 KiB
2021-03-12T02:47:00.426895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.4
Range6.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2959591209
Coefficient of variation (CV)13.66168442
Kurtosis421.2959636
Mean0.02166344294
Median Absolute Deviation (MAD)0
Skewness19.81634398
Sum11.2
Variance0.08759180124
MonotocityNot monotonic
2021-03-12T02:47:00.495910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0509
98.5%
0.82
 
0.4%
0.22
 
0.4%
0.41
 
0.2%
6.41
 
0.2%
1.41
 
0.2%
11
 
0.2%
ValueCountFrequency (%)
0509
98.5%
0.22
 
0.4%
0.41
 
0.2%
0.82
 
0.4%
11
 
0.2%
ValueCountFrequency (%)
6.41
0.2%
1.41
0.2%
11
0.2%
0.82
0.4%
0.41
0.2%

area
Real number (ℝ≥0)

ZEROS

Distinct251
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.84729207
Minimum0
Maximum1090.84
Zeros247
Zeros (%)47.8%
Memory size4.2 KiB
2021-03-12T02:47:00.592932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.52
Q36.57
95-th percentile48.714
Maximum1090.84
Range1090.84
Interquartile range (IQR)6.57

Descriptive statistics

Standard deviation63.65581847
Coefficient of variation (CV)4.954804337
Kurtosis194.1407211
Mean12.84729207
Median Absolute Deviation (MAD)0.52
Skewness12.84693353
Sum6642.05
Variance4052.063225
MonotocityNot monotonic
2021-03-12T02:47:00.697956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0247
47.8%
1.943
 
0.6%
3.712
 
0.4%
0.92
 
0.4%
1.952
 
0.4%
2.142
 
0.4%
2.182
 
0.4%
1.562
 
0.4%
9.962
 
0.4%
28.662
 
0.4%
Other values (241)251
48.5%
ValueCountFrequency (%)
0247
47.8%
0.091
 
0.2%
0.171
 
0.2%
0.211
 
0.2%
0.241
 
0.2%
ValueCountFrequency (%)
1090.841
0.2%
746.281
0.2%
278.531
0.2%
212.881
0.2%
200.941
0.2%

Interactions

2021-03-12T02:46:46.384732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:46.541767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:46.656794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:46.764818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:46.866841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:46.971864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.075888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.177911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.279934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.386958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.492982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.602006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.713032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.818055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:47.916077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.018100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.119123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.217145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.317167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.421191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.522213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.637240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.749265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.857289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:48.960312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.065336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.169359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.271382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.372405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.479429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.584453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.691477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.795502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.901525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:49.995546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.092567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.188589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.282609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.376631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.475654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.574676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.671698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.765719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.863741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:50.955762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.043781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.133803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.221822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.311842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.403862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.494883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.597906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.695929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.798952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.894974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:51.985994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.079015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.169035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.258055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.356077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.450098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.553122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.651143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.754167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.850188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:52.940208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.033230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.123250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.213270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.309292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.402313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.499335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:53.593356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.119475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.212496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.299515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.391536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.480556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.565574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.656595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.745616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.843638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:54.939659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.039681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.131702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.216721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.304742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.393762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.478780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.569801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.660822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.765845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.866868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:55.972892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.072915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.166936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.262957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.359979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.453000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.546021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.643042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.744066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.842087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:56.944110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.040132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.129153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.222173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.315194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.407215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-12T02:46:57.498235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-12T02:47:00.792977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-12T02:47:00.949013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-12T02:47:01.106048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-12T02:47:01.267084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-12T02:47:01.413116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-12T02:46:57.677275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-12T02:46:57.897325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

XYmonthdayFFMCDMCDCISItempRHwindrainarea
075marfri86.226.294.35.18.251.06.70.00.0
174octtue90.635.4669.16.718.033.00.90.00.0
274octsat90.643.7686.96.714.633.01.30.00.0
386marfri91.733.377.59.08.397.04.00.20.0
486marsun89.351.3102.29.611.499.01.80.00.0
586augsun92.385.3488.014.722.229.05.40.00.0
686augmon92.388.9495.68.524.127.03.10.00.0
786augmon91.5145.4608.210.78.086.02.20.00.0
886septue91.0129.5692.67.013.163.05.40.00.0
975sepsat92.588.0698.67.122.840.04.00.00.0

Last rows

XYmonthdayFFMCDMCDCISItempRHwindrainarea
50724augfri91.0166.9752.67.125.941.03.60.00.00
50812augfri91.0166.9752.67.125.941.03.60.00.00
50954augfri91.0166.9752.67.121.171.07.61.42.17
51065augfri91.0166.9752.67.118.262.05.40.00.43
51186augsun81.656.7665.61.927.835.02.70.00.00
51243augsun81.656.7665.61.927.832.02.70.06.44
51324augsun81.656.7665.61.921.971.05.80.054.29
51474augsun81.656.7665.61.921.270.06.70.011.16
51514augsat94.4146.0614.711.325.642.04.00.00.00
51663novtue79.53.0106.71.111.831.04.50.00.00

Duplicate rows

Most frequent

XYmonthdayFFMCDMCDCISItempRHwindrainareacount
034augsun91.4142.4601.410.619.839.05.40.00.002
136junfri91.194.1232.17.119.238.04.50.00.002
243augwed92.1111.2654.19.620.442.04.90.00.002
344marsat91.735.880.87.817.027.04.90.028.662